Physics-Aware Attacks on EV Charging Systems
Analysis
This paper addresses a critical and timely issue: the vulnerability of smart grids, specifically EV charging infrastructure, to adversarial attacks. The use of physics-informed neural networks (PINNs) within a federated learning framework to create a digital twin is a novel approach. The integration of multi-agent reinforcement learning (MARL) to generate adversarial attacks that bypass detection mechanisms is also significant. The study's focus on grid-level consequences, using a T&D dual simulation platform, provides a comprehensive understanding of the potential impact of such attacks. The work highlights the importance of cybersecurity in the context of vehicle-grid integration.
Key Takeaways
- •Proposes PHANTOM, a physics-aware adversarial network for attacking EV charging systems.
- •Employs a physics-informed neural network (PINN) within a federated learning framework.
- •Uses multi-agent reinforcement learning (MARL) to generate adversarial false data injection (FDI) strategies.
- •Demonstrates the ability of attacks to disrupt load balancing and induce voltage instabilities.
- •Highlights the need for physics-aware cybersecurity in vehicle-grid integration.
“Results demonstrate how learned attack policies disrupt load balancing and induce voltage instabilities that propagate across T and D boundaries.”